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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?

A. Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.
B. Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.
C. Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.
D. Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.

Question 2

A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A. Store all numerical features in float64 format to prevent rounding errors during transformations.
B. Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.
C. Convert all categorical variables into string representations to preserve their original format for later analysis.
D. Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.

Question 3

You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

A. Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
B. Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.
C. Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.
D. Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.

Question 4

You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

A. Enable pandas compatibility mode in cudf to improve performance.
B. Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
C. Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.
D. Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.

Question 5

A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
B. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
C. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
D. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.

Solutions: